RESUMEN
Necroptosis, which is recently recognized as a form of programmed cell death, plays a critical role in cancer biology, including tumorigenesis and cancer immunology. It was recognized not only to defend against tumor progression by suppressing adaptive immune responses but also to promote tumorigenesis and cancer metastasis after recruiting inflammatory responses. Thus the crucial role of necrosis in tumorigenesis has attracted increasing attention. Due to the heterogeneity of the tumor immune microenvironment (TIME) in lung adenocarcinoma (LUAD), the prognosis and the response to immunotherapy vary distinctly across patients, underscoring the need for a stratification algorithm for clinical practice. Although previous studies have formulated the crucial role of lncRNAs in tumorigenicity, the relationship between necroptosis-related lncRNAs, TIME, and the prognosis of patients with LUAD was still elusive. In the current study, a robust and novel prognostic stratification model based on Necroptosis-related LncRNA Risk Scoring (NecroLRS) and clinicopathological parameters was constructed and systemically validated in both internal and external validation cohorts. The expression profile of four key lncRNAs was further validated by qRT-PCR in 4 human LUAD cell lines. And a novel immune landscape alteration was observed between NecroLRS-High and -Low patients. To further elucidate the mechanism of necroptosis in the prognosis of LUAD from a single-cell perspective, a novel stratification algorithm based on K-means clustering was introduced to extract both malignant and NecroLRS-High subsets from epithelial cells. And the necroptosis-related immune infiltration landscape and developmental trajectory were investigated respectively. Critically, NecroLRS was found to be positively correlated with neutrophil enrichment, inflammatory immune response, and malignant phenotypes of LUAD. In addition, novel ligand-receptor pairs between NecroLRS-High cells and other immunocytes were investigated and optimal therapeutic compounds were screened to provide potential targets for future studies. Taken together, our findings reveal emerging mechanisms of necroptosis-induced immune microenvironment alteration on the deteriorative prognosis and may contribute to improved prognosis and individualized precision therapy for patients with LUAD.
RESUMEN
Introduction: The aim of this work was to determine the feasibility of using a deep learning approach to predict occult lymph node metastasis (OLM) based on preoperative FDG-PET/CT images in patients with clinical node-negative (cN0) lung adenocarcinoma. Materials and Methods: Dataset 1 (for training and internal validation) included 376 consecutive patients with cN0 lung adenocarcinoma from our hospital between May 2012 and May 2021. Dataset 2 (for prospective test) used 58 consecutive patients with cN0 lung adenocarcinoma from June 2021 to February 2022 at the same center. Three deep learning models: PET alone, CT alone, and combined model, were developed for the prediction of OLM. The performance of the models was evaluated on internal validation and prospective test in terms of accuracy, sensitivity, specificity, and areas under the receiver operating characteristic curve (AUCs). Results: The combined model incorporating PET and CT showed the best performance, achieved an AUC of 0.81 [95% confidence interval (CI): 0.61, 1.00] in the prediction of OLM in internal validation set (n = 60) and an AUC of 0.87 (95% CI: 0.75, 0.99) in the prospective test set (n = 58). The model achieved 87.50% sensitivity, 80.00% specificity, and 81.00% accuracy in the internal validation set and achieved 75.00% sensitivity, 88.46% specificity, and 86.60% accuracy in the prospective test set. Conclusion: This study presented a deep learning approach to enable the prediction of occult nodal involvement based on the PET/CT images before surgery in cN0 lung adenocarcinoma, which would help clinicians select patients who would be suitable for sublobar resection.